Sensory regulation of network components underlying ciliary locomotion in Hermissenda.

Ciliary locomotion in the nudibranch mollusk Hermissenda is modulated by the visual and graviceptive systems. Components of the neural network mediating ciliary locomotion have been identified including aggregates of polysensory interneurons that receive monosynaptic input from identified photoreceptors and efferent neurons that activate cilia. Illumination produces an inhibition of type I(i) (off-cell) spike activity, excitation of type I(e) (on-cell) spike activity, decreased spike activity in type III(i) inhibitory interneurons, and increased spike activity of ciliary efferent neurons. Here we show that pairs of type I(i) interneurons and pairs of type I(e) interneurons are electrically coupled. Neither electrical coupling or synaptic connections were observed between I(e) and I(i) interneurons. Coupling is effective in synchronizing dark-adapted spontaneous firing between pairs of I(e) and pairs of I(i) interneurons. Out-of-phase burst activity, occasionally observed in dark-adapted and light-adapted pairs of I(e) and I(i) interneurons, suggests that they receive synaptic input from a common presynaptic source or sources. Rhythmic activity is typically not a characteristic of dark-adapted, light-adapted, or light-evoked firing of type I interneurons. However, burst activity in I(e) and I(i) interneurons may be elicited by electrical stimulation of pedal nerves or generated at the offset of light. Our results indicate that type I interneurons can support the generation of both rhythmic activity and changes in tonic firing depending on sensory input. This suggests that the neural network supporting ciliary locomotion may be multifunctional. However, consistent with the nonmuscular and nonrhythmic characteristics of visually modulated ciliary locomotion, type I interneurons exhibit changes in tonic activity evoked by illumination.

Coronary artery stents: influence of adaptive statistical iterative reconstruction on image quality using 64-HDCT

OBJECTIVE: The assessment of coronary stents with present-generation 64-detector row computed tomography (HDCT) scanners is limited by image noise and blooming artefacts. We evaluated the performance of adaptive statistical iterative reconstruction (ASIR) for noise reduction in coronary stent imaging with HDCT. METHODS AND RESULTS: In 50 stents of 28 patients (mean age 64 ± 10 years) undergoing coronary CT angiography (CCTA) on an HDCT scanner the mean in-stent luminal diameter, stent length, image quality, in-stent contrast attenuation, and image noise were assessed. Studies were reconstructed using filtered back projection (FBP) and ASIR-FBP composites. ASIR resulted in reduced image noise vs. FBP (P < 0.0001). Two readers graded the CCTA stent image quality on a 4-point Likert scale and determined the proportion of interpretable stent segments. The best image quality for all clinical images was obtained with 40 and 60% ASIR with significantly larger luminal area visualization compared with FBP (+42.1 ± 5.4% with 100% ASIR vs. FBP alone; P < 0.0001) while the stent length was decreased (-4.7 ± 0.9%,

Volume and shape in feature space on adaptive FCM in MRI segmentation.

Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster's shape in the feature space are incorporated into the fuzzy c-means (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.

Adaptive Molecular Evolution in the Opsin Genes of Rapidly Speciating Cichlid Species

Cichlid fish inhabit a diverse range of environments that vary in the spectral content of light available for vision. These differences should result in adaptive selective pressure on the genes involved in visual sensitivity, the opsin genes. This study examines the evidence for differential adaptive molecular evolution in East African cichlid opsin genes due to gross differences in environmental light conditions. First, we characterize the selective regime experienced by cichlid opsin genes using a likelihood ratio test format, comparing likelihood models with different constraints on the relative rates of amino acid substitution, across sites. Second, we compare turbid and clear lineages to determine if there is evidence of differences in relative rates of substitution. Third, we present evidence of functional diversification and its relationship to the photic environment among cichlid opsin genes. We report statistical evidence of positive selection in all cichlid opsin genes, except short wavelength–sensitive 1 and short wavelength–sensitive 2b. In all genes predicted to be under positive selection, except short wavelength–sensitive 2a, we find differences in selective pressure between turbid and clear lineages. Potential spectral tuning sites are variable among all cichlid opsin genes; however, patterns of substitution consistent with photic environment–driven evolution of opsin genes are observed only for short wavelength–sensitive 1 opsin genes. This study identifies a number of promising candidate-tuning sites for future study by site-directed mutagenesis. This work also begins to demonstrate the molecular evolutionary dynamics of cichlid visual sensitivity and its relationship to the photic environment.

Hybridization and adaptive radiation

Whether interspecific hybridization is important as a mechanism that generates biological diversity is a matter of controversy. Whereas some authors focus on the potential of hybridization as a source of genetic variation, functional novelty and new species, others argue against any important role, because reduced fitness would typically render hybrids an evolutionary dead end. By drawing on recent developments in the genetics and ecology of hybridization and on principles of ecological speciation theory, I develop a concept that reconciles these views and adds a new twist to this debate. Because hybridization is common when populations invade new environments and potentially elevates rates of response to selection, it predisposes colonizing populations to rapid adaptive diversification under disruptive or divergent selection. I discuss predictions and suggest tests of this hybrid swarm theory of adaptive radiation and review published molecular phylogenies of adaptive radiations in light of the theory. Some of the confusion about the role of hybridization in evolutionary diversification stems from the contradiction between a perceived necessity for cessation of gene flow to enable adaptive population differentiation on the one hand [1], and the potential of hybridization for generating adaptive variation, functional novelty and new species 2, 3 and 4 on the other. Much progress in the genetics 5, 6, 7, 8 and 9 and ecology of hybridization 9, 10 and 11, and in our understanding of the role of ecology in speciation (see Glossary) 12, 13 and 14 make a re-evaluation timely. Whereas botanists traditionally stressed the diversity-generating potential of hybridization 2, 3 and 14, zoologists traditionally saw it as a process that limits diversification [1] and refer to it mainly in the contexts of hybrid zones (Box 1) and reinforcement of reproductive isolation [15]. Judging by the wide distribution of allopolyploidy among plants, many plant species might be of direct hybrid origin or descended from a hybrid species in the recent past [16]. The ability to reproduce asexually might explain why allopolyploid hybrid species are more common in plants than in animals. Allopolyploidy arises when meiotic mismatch of parental chromosomes or karyotypes causes hybrid sterility. Mitotic error, duplicating the karyotype, can restore an asexually maintained hybrid line to fertility. Although bisexual allopolyploid hybrid species are not uncommon in fish [17] and frogs [18], the difficulty with which allopolyploid animals reproduce, typically requiring gynogenesis[19], makes establishment and survival of allopolyploid animal species difficult.

CAOR: Context-aware Adaptive Opportunistic Routing in Mobile Ad-hoc Networks

Opportunistic routing (OR) employs a list of candidates to improve wireless transmission reliability. However, conventional list-based OR restricts the freedom of opportunism, since only the listed nodes are allowed to compete for packet forwarding. Additionally, the list is generated statically based on a single network metric prior to data transmission, which is not appropriate for mobile ad-hoc networks (MANETs). In this paper, we propose a novel OR protocol - Context-aware Adaptive Opportunistic Routing (CAOR) for MANETs. CAOR abandons the idea of candidate list and it allows all qualified nodes to participate in packet transmission. CAOR forwards packets by simultaneously exploiting multiple cross-layer context information, such as link quality, geographic progress, energy, and mobility.With the help of the Analytic Hierarchy Process theory, CAOR adjusts the weights of context information based on their instantaneous values to adapt the protocol behavior at run-time. Moreover, CAOR uses an active suppression mechanism to reduce packet duplication. Simulation results show that CAOR can provide efficient routing in highly mobile environments. The adaptivity feature of CAOR is also validated.

Locomotor velocity and striatal adaptive gene expression changes of the direct and indirect pathways in Parkinsonian rats

BACKGROUND In Parkinson's disease (PD), bradykinesia, or slowness of movement, only appears after a large striatal dopamine depletion. Compensatory mechanisms probably play a role in this delayed appearance of symptoms. OBJECTIVE Our hypothesis is that the striatal direct and indirect pathways participate in these compensatory mechanisms. METHODS We used the unilateral 6-hydroxydopamine (6-OHDA) rat model of PD and control animals. Four weeks after the lesion, the spontaneous locomotor activity of the rats was measured and then the animals were killed and their brain extracted. We quantified the mRNA expression of markers of the striatal direct and indirect pathways as well as the nigral expression of dopamine transporter (DAT) and tyrosine hydroxylase (TH) mRNA. We also carried out an immunohistochemistry for the striatal TH protein expression. RESULTS As expected, the unilateral 6-OHDA rats presented a tendency to an ipsilateral head turning and a low locomotor velocity. In 6-OHDA rats only, we observed a significant and positive correlation between locomotor velocity and both D1-class dopamine receptor (D1R) (direct pathway) and enkephalin (ENK) (indirect pathway) mRNA in the lesioned striatum, as well as between D1R and ENK mRNA. CONCLUSIONS Our results demonstrate a strong relationship between both direct and indirect pathways and spontaneous locomotor activity in the parkinsonian rat model. We suggest a synergy between both pathways which could play a role in compensatory mechanisms and may contribute to the delayed appearance of bradykinesia in PD.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.

A randomised double-blind, cross-over trial of 4-aminopyridine for downbeat nystagmus--effects on slowphase eye velocity, postural stability, locomotion and symptoms

Objective The effects of 4-aminopyridine (4-AP) on downbeat nystagmus (DBN) were analysed in terms of slow-phase velocity (SPV), stance, locomotion, visual acuity (VA), patient satisfaction and side effects using standardised questionnaires. Methods Twenty-seven patients with DBN received 5 mg 4-AP four times a day or placebo for 3 days and 10 mg 4-AP four times a day or placebo for 4 days. Recordings were done before the first, 60 min after the first and 60 min after the last drug administration. Results SPV decreased from 2.42 deg/s at baseline to 1.38 deg/s with 5 mg 4-AP and to 2.03 deg/s with 10 mg 4-AP (p<0.05; post hoc: 5 mg 4-AP: p=0.04). The rate of responders was 57%. Increasing age correlated with a 4-AP-related decrease in SPV (p<0.05). Patients improved in the ‘get-up-and-go test’ with 4-AP (p<0.001; post hoc: 5 mg: p=0.025; 10 mg: p<0.001). Tandem-walk time (both p<0.01) and tandem-walk error (4-AP: p=0.054; placebo: p=0.059) improved under 4-AP and placebo. Posturography showed that some patients improved with the 5 mg 4-AP dose, particularly older patients. Near VA increased from 0.59 at baseline to 0.66 with 5 mg 4-AP (p<0.05). Patients with idiopathic DBN had the greatest benefit from 4-AP. There were no differences between 4-AP and placebo regarding patient satisfaction and side effects. Conclusions 4-AP reduced SPV of DBN, improved near VA and some locomotor parameters. 4-AP is a useful medication for DBN syndrome, older patients in particular benefit from the effects of 5 mg 4-AP on nystagmus and postural stability.

Real-World Evaluation of Sensor Context-aware Adaptive Duty-cycled Opportunistic Routing

Energy is of primary concern in wireless sensor networks (WSNs). Low power transmission makes the wireless links unreliable, which leads to frequent topology changes. Resulting packet retransmissions aggravate the energy consumption. Beaconless routing approaches, such as opportunistic routing (OR) choose packet forwarders after data transmissions, and are promising to support dynamic features of WSNs. This paper proposes SCAD - Sensor Context-aware Adaptive Duty-cycled beaconless OR for WSNs. SCAD is a cross-layer routing solution and it brings the concept of beaconless OR into WSNs. SCAD selects packet forwarders based on multiple types of network contexts. To achieve a balance between performance and energy efficiency, SCAD adapts duty-cycles of sensors based on real-time traffic loads and energy drain rates. We implemented SCAD in TinyOS running on top of Tmote Sky sensor motes. Real-world evaluations show that SCAD outperforms other protocols in terms of both throughput and network lifetime.

Context-Aware Adaptive Opportunistic Routing in Mobile Ad-hoc and Sensor Networks

Mobile ad-hoc networks (MANETs) and wireless sensor networks (WSNs) have been attracting increasing attention for decades due to their broad civilian and military applications. Basically, a MANET or WSN is a network of nodes connected by wireless communication links. Due to the limited transmission range of the radio, many pairs of nodes in MANETs or WSNs may not be able to communicate directly, hence they need other intermediate nodes to forward packets for them. Routing in such types of networks is an important issue and it poses great challenges due to the dynamic nature of MANETs or WSNs. On the one hand, the open-air nature of wireless environments brings many difficulties when an efficient routing solution is required. The wireless channel is unreliable due to fading and interferences, which makes it impossible to maintain a quality path from a source node to a destination node. Additionally, node mobility aggravates network dynamics, which causes frequent topology changes and brings significant overheads for maintaining and recalculating paths. Furthermore, mobile devices and sensors are usually constrained by battery capacity, computing and communication resources, which impose limitations on the functionalities of routing protocols. On the other hand, the wireless medium possesses inherent unique characteristics, which can be exploited to enhance transmission reliability and routing performance. Opportunistic routing (OR) is one promising technique that takes advantage of the spatial diversity and broadcast nature of the wireless medium to improve packet forwarding reliability in multihop wireless communication. OR combats the unreliable wireless links by involving multiple neighboring nodes (forwarding candidates) to choose packet forwarders. In opportunistic routing, a source node does not require an end-to-end path to transmit packets. The packet forwarding decision is made hop-by-hop in a fully distributed fashion. Motivated by the deficiencies of existing opportunistic routing protocols in dynamic environments such as mobile ad-hoc networks or wireless sensor networks, this thesis proposes a novel context-aware adaptive opportunistic routing scheme. Our proposal selects packet forwarders by simultaneously exploiting multiple types of cross-layer context information of nodes and environments. Our approach significantly outperforms other routing protocols that rely solely on a single metric. The adaptivity feature of our proposal enables network nodes to adjust their behaviors at run-time according to network conditions. To accommodate the strict energy constraints in WSNs, this thesis integrates adaptive duty-cycling mechanism to opportunistic routing for wireless sensor nodes. Our approach dynamically adjusts the sleeping intervals of sensor nodes according to the monitored traffic load and the estimated energy consumption rate. Through the integration of duty cycling of sensor nodes and opportunistic routing, our protocol is able to provide a satisfactory balance between good routing performance and energy efficiency for WSNs.